How data normalization affects machine learning performance

A model performs well in testing, clears review, and ships to production, only for predictions to start drifting within weeks. The culprit is usually not the algorithm or the training data but a normalization step applied during development and handled differently in the inference pipeline.The failure is common and avoidable. Data normalization in machine learning is a design decision that directly affects whether a model trains efficiently, generalizes reliably, and holds up in production.